MrCoffee7

It is a little hard to give you answers as the articles are open access but the search function on the ACM website is paywalled!

I only see a few articles listed compared to the amount of new AI and ML articles that come out every day. Are most of them being filtered out of your feed and what are the criteria for them being excluded?

It would be great if the results table had a column for whether or not the software had a public web endpoint to actually run the software without installing it locally yourself.

No, I don't think you are going to find such a tool. For one thing, there is a subculture of people who write and vlog about AI tools, and sometimes they offer affliate weblinks with discount codes for them. It would be too much trouble for anyone to identify all those people and scrape all their content daily.

I'm confused by the names in the credits? Are the names all made up names of AI actors, or are there some humans in the video mixed in with AI actors?

Expert systems don't work better than LLMs - they are very brittle and completely fall apart when they encounter a novel situation outside the scenarios programmed into their if-then rules.

Do you have any references that further explain what MIND and the "Last Economy" concepts are? Also, any references on "valuation tension" or "expectation saturation" as I do not understand what you are trying to measure?

It looks like metot.org uses a vote counting type approach to evaluate how many PRO and CON arguments were made and then uses the vote count to evaluate the strength of the argument. There are a lot of problems with this approach. Vote counting fails because: Epistemic naivety: Ignores evidence quality ; Structural blindness: Misses dialectical interactions; Semantic impoverishment: No context, no warrants, no hedging; Temporal insensitivity: Static snapshots of dynamic discourse; Fallacy tolerance: No rhetorical/logical error detection

Proper system requires: Deep NLP: Discourse parsing, semantic role labeling, entailment; Structured reasoning: AAF, probabilistic argumentation, Bayesian aggregation; Domain knowledge: Evidence hierarchies, causal inference, statistical meta-analysis; Explainability: Attention visualization, counterfactual reasoning, gradient-based saliency

That is not what other sources show. https://www.visualcapitalist.com/cp/ai-competitiveness-by-co...

There is no weblink to the Terms of Service, nor are there any weblinks giving a blurb about why one would want to sign into PaperTrails and what its use case is?

It seems like each article takes a lot of vertical space. What if you changed the UI so you just displayed a title for each article with a little icon next to it like a downward arrow that you could click on if you wanted to see more of that article? That way, you could display more titles on one screen so that the user could more quickly scroll to the articles they actually wanted to see?

This sounds like a terrible idea. I guess people have already forgotten about the East Palestine, Ohio train derailment carrying hazardous materials and the disaster it caused for that town https://en.wikipedia.org/wiki/East_Palestine,_Ohio,_train_de... .

It looks like increased ChatGPT error rates are a fairly common event over time: https://status.openai.com/history

It looks like you can't actually navigate to see the weblink of a specific poster from your app unless you registered for the NeurIPS conference?

This is a great application and seems to give much better answers than trying to figure this out yourself by looking at Google Scholar information.

They haven't gone down - it is just that they are being disclosed less. Groups like this one track this: https://www.idtheftcenter.org

What does this weblink do - it seemed to change my Hacker News feed and the articles in it that I was seeing?

The conclusion of this article is false. There are many counterexamples to it. Just one example - an AI generated song topped the Spotify chart https://www.forbes.com/sites/conormurray/2025/11/20/apparent...

Why isn't there a dependency graph for the example paper "Sheaf Cohomology of Linear Predictive Coding Networks"?

I wonder if the field of AI and ML need to develop additional awards and prized beyond the current ones? For example, in the field of math one of the reasons that additional prizes and awards were developed after the Fields Medal was because of perceived limitations and inequities in the Fields Medal system.

So does Jürgen Schmidhuber feel like the Rosalind Franklin of AI? Are there very many other AI and ML researchers who feel similarly overlooked for their contributions and that their ideas have been plagiarized? I'm also curious about how science historians deal with these types of issues of credit and priority for originating ideas, and how long it usually takes to correct the record. It seems that in the case of many women overlooked for their contributions to science that it can take decades.

It looks like a great tool. It would be even better if the mindmap feature integrated URLs and images for even better explanations.

There is no feedback button and no search tool for it.

Clickbait!

Mainframes in the early 1980s allowed people to access computer power via interactive terminals instead of sitting at a keypunch machine creating large decks of punched cards to take over to a card reader machine. This was before personal computers were widely available at an affordable price, so office workers did not have a personal PC sitting at their cubicle at that point. The computer monitors at that point were not full color displays - they just had bright green text against a black screen. The language used on the mainframe was COBOL. If your computer job required tapes, there would be a short wait for someone to manually mount the correct tapes needed. You would need to manually go to a room near the computer to sort through printouts to pick up your specific printout of your job yourself, where the job was printed on wide sheets of white and green paper in a font that looked like something from a high-speed dot matrix quality printer. Your printout stack might have been especially heavy and thick if your program terminated abnormally with an ABEND type of error and a sysdump printed out. For references, see the books "Mainframes, Computing on Big Iron" by Patrick Stakem and "IBM's 360 and Early 370 Systems" by Emerson W. Pugh.

see this "How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison" https://arxiv.org/abs/2510.26899

You can still ask questions for medical advice. You just need to phrase the question more like a hypothetical one instead of making it obvious that you are asking for yourself.

same error here - they probably crashed because of inadequate server capacity